Existing recommendation systems, such as, but not limited to, Netflix five star ratings, Pandora like/dislike ratings, and other systems, utilize roughly the following methodology:                1) Illicit feedback for a given ‘product.’ Since this is feedback for a single product the feedback is necessarily delivered as some type of score (for example, like vs. don't like, one star vs. two stars vs. five stars, and other types of scores).        2) Build a customer profile based on how the customer rated various products.        3) Make recommendations to customers by first finding customers ‘like’ the customer in question and then recommending products that similar customers rated highly. The notion of ‘likeness’ here is typically defined on the basis of similarity in ratings across rated products.        
This prior system has a number of flaws, the most obvious of which is the following: what ‘three stars’ for a particular product might mean varies both across customers (for example, five stars for a first reviewer is equivalent to three stars for a second reviewer) as well as over time for a given customer (for example, mood might determine how generous a reviewer is with stars). This makes it essentially impossible to get a high fidelity picture of the relative likes and dislikes of a customer both because it becomes difficult to find ‘like’ customers, and also because the language the customer uses in relating her preferences is itself changing.
Therefore, there is a need for a system and method for providing personalized recommendations.